{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T20:15:34Z","timestamp":1760300134881,"version":"build-2065373602"},"reference-count":14,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,28]],"date-time":"2018-12-28T00:00:00Z","timestamp":1545955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007684","name":"Ministry of Education and Science of Ukraine","doi-asserted-by":"publisher","award":["F75\/144-2018"],"award-info":[{"award-number":["F75\/144-2018"]}],"id":[{"id":"10.13039\/501100007684","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Trainable visual navigation systems based on deep learning demonstrate potential for robustness of onboard camera parameters and challenging environment. However, a deep model requires substantial computational resources and large labelled training sets for successful training. Implementation of the autonomous navigation and training-based fast adaptation to the new environment for a compact drone is a complicated task. The article describes an original model and training algorithms adapted to the limited volume of labelled training set and constrained computational resource. This model consists of a convolutional neural network for visual feature extraction, extreme-learning machine for estimating the position displacement and boosted information-extreme classifier for obstacle prediction. To perform unsupervised training of the convolution filters with a growing sparse-coding neural gas algorithm, supervised learning algorithms to construct the decision rules with simulated annealing search algorithm used for finetuning are proposed. The use of complex criterion for parameter optimization of the feature extractor model is considered. The resulting approach performs better trajectory reconstruction than the well-known ORB-SLAM. In particular, for sequence 7 from the KITTI dataset, the translation error is reduced by nearly 65.6% under the frame rate 10 frame per second. Besides, testing on the independent TUM sequence shot outdoors produces a translation error not exceeding 6% and a rotation error not exceeding 3.68 degrees per 100 m. Testing was carried out on the Raspberry Pi 3+ single-board computer.<\/jats:p>","DOI":"10.3390\/data4010004","type":"journal-article","created":{"date-parts":[[2018,12,28]],"date-time":"2018-12-28T11:52:42Z","timestamp":1545997962000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["The Model and Training Algorithm of Compact Drone Autonomous Visual Navigation System"],"prefix":"10.3390","volume":"4","author":[{"given":"Viacheslav","family":"Moskalenko","sequence":"first","affiliation":[{"name":"Department of Computer Science, Sumy State University, 40007 Sumy, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3443-3990","authenticated-orcid":false,"given":"Alona","family":"Moskalenko","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sumy State University, 40007 Sumy, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3239-1977","authenticated-orcid":false,"given":"Artem","family":"Korobov","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sumy State University, 40007 Sumy, Ukraine"}]},{"given":"Viktor","family":"Semashko","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sumy State University, 40007 Sumy, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Moskalenko, V., Moskalenko, A., Korobov, A., Boiko, O., Martynenko, S., and Borovenskyi, O. (2018, January 21\u201325). Model and Training Methods of Autonomous Navigation System for Compact Drones. Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine.","DOI":"10.1109\/DSMP.2018.8478521"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Suwandi, B., Kitasuka, T., and Aritsugi, M. (2017, January 5\u20138). Low-cost IMU and GPS fusion strategy for apron vehicle positioning. Proceedings of the 2017 IEEE Region 10 Conference (TENCON), Penang, Malaysia.","DOI":"10.1109\/TENCON.2017.8227906"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, S., Deng, Z., and Yin, G. (2016). An Accurate GPS-IMU\/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints. Sensors, 16.","DOI":"10.3390\/s16030280"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mary, B.A., and Gerhard, P.H. (2017). Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter. Sensors, 17.","DOI":"10.3390\/s17102164"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Folkesson, J., Leederkerken, J., Williams, R., Patrikalakis, A., and Leonard, J.A. (2008). A Feature Based Navigation System for an Autonomous Underwater Robot. Springer Tracts in Advanced Robotics, Proceedings of the Sixth Edition of Field and Service Robotics, Chamonix, France, 9\u201312 July 2007, Springer.","DOI":"10.1007\/978-3-540-75404-6_10"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","article-title":"Past, Present, and Future of Simultaneous Localization and Mapping: Towards the Robust-Perception Age","volume":"32","author":"Cadena","year":"2016","journal-title":"IEEE Trans. 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Available online: https:\/\/arxiv.org\/pdf\/1611.06069.pdf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1016\/j.neucom.2008.11.027","article-title":"Sparse Coding Neural Gas: Learning of Overcomplete Data Representations","volume":"72","author":"Labusch","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3403","DOI":"10.1109\/TCYB.2018.2830338","article-title":"Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount","volume":"48","author":"Zou","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_12","first-page":"38","article-title":"Information-extreme algorithm of the system for recognition of objects on the terrain with optimization parameter feature extraction","volume":"2","author":"Moskalenko","year":"2017","journal-title":"Radio Electron. Comput. Sci. Control"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"19","DOI":"10.15587\/1729-4061.2018.139923","article-title":"Improving the effectiveness of training the on-board object detection system for a compact unmanned aerial vehicle","volume":"4","author":"Moskalenko","year":"2018","journal-title":"Eastern-Eur. J. Enterp. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vens, C., and Costa, F. (2011, January 11\u201314). Random Forest Based Feature Induction. Proceedings of the IEEE 11th International Conference on Data Mining, VA, Canada.","DOI":"10.1109\/ICDM.2011.121"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/1\/4\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:36:29Z","timestamp":1760196989000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/1\/4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,28]]},"references-count":14,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["data4010004"],"URL":"https:\/\/doi.org\/10.3390\/data4010004","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2018,12,28]]}}}